Raptor: Large Scale Analysis of Big Raster and Vector Data
Summary: Raptor enables large-scale zonal statistics without raster–vector conversion by directly combining raster and vector data. It benchmarks three approaches—vector-based, raster-based, and Raptor—showing how cross-representation efficiency varies with dataset size. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Samriddhi Singla (University of California Riverside)
- 2. Ahmed Eldawy (University of California Riverside)
- 3. Rami Alghamdi (University of Minnesota)
- 4. Mohamed F. Mokbel (Qatar Computing Research Institute; University of Minnesota)
BibTeX Citation
@article{singla_vldb19,
title = {{Raptor: Large Scale Analysis of Big Raster and Vector Data}},
author = {Singla, Samriddhi and Eldawy, Ahmed and Alghamdi, Rami and Mokbel, Mohamed F.},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {12},
pages = {1950--1953},
doi = {10.14778/3352063.3352107},
url = {https://doi.org/10.14778/3352063.3352107},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,961 | Array DBMS: Past, Present, and (Near) Future | 2021 | VLDB | 5.5181056e-05 |
| 11,088 | RDPro: Distributed Processing of Big Raster Data | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 724 | The Multidimensional Database System RasDaMan | 1998 | SIGMOD | 0.00014620119 |
| 1,175 | Simba: Efficient In-Memory Spatial Analytics | 2016 | SIGMOD | 0.00011812263 |
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